Multi-fingered robot grasping is a challenging problem that is difficult to tackle using hand-coded programs. In this paper we present an imitation learning approach for learning and generalizing grasping skills based on human demonstrations. To this end, we split the task of synthesizing a grasping motion into three parts: (1) learning efficient grasp representations from human demonstrations, (2) warping contact points onto new objects, and (3) optimizing and executing the reach-and-grasp movements. We learn low-dimensional latent grasp spaces for different grasp types, which form the basis for a novel extension to dynamic motor primitives. These latent-space dynamic motor primitives are used to synthesize entire reach-and-grasp movements. We evaluated our method on a real humanoid robot. The results of the experiment demonstrate the robustness and versatility of our approach.
Generalization of human grasping for multi-fingered robot hands
2012
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Springer Verlag | 2018
|Optimum grasping planning for multi-fingered dextrous hands based on the asymptotical stability
British Library Online Contents | 1996
|Structural Synthesis of Multi-Fingered Hands
Online Contents | 2002
|Dynamic Manipulation/Grasping Control of Multifingered Robot Hands
British Library Online Contents | 1994
|NTRS | 1984
|